Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Abstract

Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, realtime freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential opensource option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

“…There is increasing consensus among scientists as to what represent best practices that should be followed when producing, evaluating, and communicating ecological forecasts (Dietze et al 2018, Harris et al 2018, White et al 2019, Carey et al 2021. Some of those practices have been at the core of this work and we discussed their importance extensively in previous sections, including explicitly accounting for and propagating multiple sources of uncertainty, such as observation and process uncertainty, identifying better predictor variables that are expected to relate to the forecast endpoint, using the model to make both short-and long-term predictions to accommodate the time scales of management decisions while also using short-term forecasts to facilitate evaluation of model performance, and routinely assessing and updating the model with new data (Dietze et al 2018, Harris et al 2018, White et al 2019.…”

“…2019, Carey et al. 2021). Guided by recent appreciation for the spatial distribution of nutrient sources that affect the Bay’s water quality, how loads have changed over time, and the complex intra‐annual variability in hypoxia, we explore how model performance changes when different combinations of HV metrics, TN load sources, and TN load time windows are used as calibration inputs.…”

“…In this work, we build on the Chesapeake Bay hypoxia case study and present an enhanced version of the forecasting model that addresses some of the most critical challenges, opportunities, and best practices of contemporary ecological forecasting. These include identifying predictors and metrics of ecosystem state that improve model performance and management relevance, explicitly accounting for and propagating multiple sources of uncertainty, evaluating forecasting performance through hindcasting, and applying the model to answer management questions (Dietze et al 2018, Harris et al 2018, White et al 2019, Carey et al 2021. Guided by recent appreciation for the spatial distribution of nutrient sources that affect the Bay's water quality, how loads have changed over time, and the complex intra-annual variability in hypoxia, we explore how model performance changes when different combinations of HV metrics, TN load sources, and TN load time windows are used as calibration inputs.…”

SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for Smart Monitoring of Water Environments

“…The goal of the work presented in [33] is the implementation of near-term and iterative ecological predictions for freshwater management. A forecasting framework named FLARE was developed to help manage water quality in critical lakes and reservoir ecosystems.…”

Opportunities for seasonal forecasting to support water management outside the tropics

“…in a number of lake water quality studies (e.g. Carey et al, 2021;Thomas et al, 2020), but seasonal time-scales have not been addressed to our knowledge. The focus of the WATExR project, a European Union (EU) project funded by ERA4CS, was therefore to help address this gap.…”